Is anybody too big to fail? (Answer: no.) What actually keeps a business alive when everything around it starts shifting?

In this ep, Andrew and Jay talk the quiet reality behind “too big to fail,” looking at why companies collapse, how bad assumptions creep in, and what it takes to stay standing when conditions change fast. From supply chain headaches and rising material costs to vendor missteps and risky investments, they look at the everyday decisions that shape whether a shop survives or struggles.

Along the way, they dig into why you can’t afford to coast, how small operational choices add up, and what it really means to adapt in a changing market. The conversation even takes a turn into brain performance and decision-making, exploring how the way you’re wired affects how you lead.

Jay and Andrew start by talking about getting back into work after time away, which leads into a broader discussion about applying work systems and efficiency principles in both the shop and at home. They cover a range of topics including garage organization, equipment decisions, troubleshooting production issues, trade shows, business growth, tools, taxes, and the use of AI, and more.

Fresh off the Machining Summit on the Summit in Mammoth Lakes, Andrew and Jay sit down to unpack what actually made the experience worth it...and it wasn’t just the sessions. From gondola rides to small, living-room-style conversations, they talk about how being in the right environment with the right people leads to better conversations, clearer thinking, and relationships that actually matter.

Along the way, they share some of the bigger takeaways that stuck with them, including why collaboration tends to win over competition, where they see the industry heading, real-world lessons on finances, building redundancy into your operations, and more.

What happens when your factory catches fire without warning? In this episode, Brian Meyers (president of Fat American Mfg and host of Lean by Doing podcast) sits down with Jay and Andrew to share the story of a fire that broke out in his brand-new facility, and what it revealed about leadership, preparation, and the power of lean manufacturing.

In short: what could have been a total loss wasn't at all. Because of the systems already in place due to a culture shaped by lean thinking, his team didn’t panic. They acted. And their decisions prevented far greater damage.

But the story doesn’t end with the fire. Brian walks through the long aftermath: the disruption, the emotional weight on the team, the insurance process, and the slow return to normal operations. Along the way, he reflects on what he would and wouldn’t change, and how the experience reshaped his thinking on safety, culture, and resilience.

Learn more from Brian:

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Jay begins by talking about selling a machine, and when it's better to go direct versus using a dealer, with broader implications regarding alignment, control, and the hidden costs of outsourcing parts of your business.

From there, the discussion shifts into shop operations: flow vs. batching, tool changes, and where efficiency actually comes from in real production environments. Jay and Andrew challenge common assumptions, showing how context matters: sometimes batching wins, sometimes ergonomics matter more than cycle time, and often the biggest gains come from reducing friction for the operator, not chasing theoretical efficiency. Plus: the perfect keyboard, how to get that most out of a conference or summit, and more.

Jay and Andrew begin with a deceptively simple question: what actually makes a company “lean”? Starting with a quote from Shigeo Shingo, they challenge the common misconception that lean is just Kanban, and explore the deeper reality that lean is less about specific tools and more about principles, tradeoffs, and context.

From there, Andrew shares a deep dive into labor tracking and ERP data, uncovering how much work was happening that never made it into cost calculations, and why “door-to-door” time matters more than overly segmented tracking. Jay pushes back with the tension every shop feels: data is only valuable if it leads to action, and too much friction in systems can break team buy-in entirely.

The episode then shifts into Andrew’s current challenge: producing tight-tolerance parts that his team can’t fully verify in-house. They take a candid look at outsourcing vs. vertical integration, the true cost of CMM capability, and the uncomfortable position of shipping parts you can’t independently validate. Jay talks about why he bought a CMM earlier than expected, what he regrets, and how fast feedback loops can change everything.

Andrew shares insights with Jay from a recent lean-focused shop tour with Paul Akers. The conversation goes to the hidden dangers of batch processing vs. one-piece flow, why takt time can matter more than cycle time, how to identify and eliminate waste at the micro-task level, and why “don’t solve problems until they exist” is often the best strategy They also explore practical challenges like line balancing, inspection differences (CMM vs. vision systems), and the surprising complexity of measuring quality in manufacturing.

Plus, a candid discussion on whether shop tours actually scale, charging for tours vs. giving them away, and turning knowledge into a valuable product instead of a free commodity.

Why do some people naturally notice problems while others don’t? Andrew introduces ideas from the book Living Sensationally, exploring how different sensory personalities affect how workers perceive disorder and opportunities for improvement.

Andrew also shares the results of his shop’s first full week of 8 a.m. morning meetings followed by shop-wide 3S, complete with funky music and a noticeable surge in improvement activity. Jay and Andrew discuss how creating space for small improvements can build momentum, and why the real goal of cleaning isn’t cleanliness, but exposing hidden problems.

They also compare notes on using AI in manufacturing environments, including Andrew’s first experiments with Claude to automate CNC workflows and program an Andon status light for his workstation. Does AI have a lot of promise as a technical collaborator? Does it also have a lot of frustrations? You bet.